{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Sebastian Raschka](http://www.sebastianraschka.com)\n",
    "\n",
    "[back](https://github.com/rasbt/matplotlib-gallery) to the `matplotlib-gallery` at [https://github.com/rasbt/matplotlib-gallery](https://github.com/rasbt/matplotlib-gallery)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%load_ext watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: 07/10/2015 \n",
      "\n",
      "CPython 3.4.3\n",
      "IPython 3.2.0\n",
      "\n",
      "matplotlib 1.4.3\n",
      "numpy 1.9.2\n"
     ]
    }
   ],
   "source": [
    "%watermark -u -v -d -p matplotlib,numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"1.5em\">[More info](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/ipython_magic/watermark.ipynb) about the `%watermark` extension</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib Formatting III: What it takes to become a legend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> I won't be a rock star. I will be a legend.\n",
    "\n",
    "-- *Freddie Mercury*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [Back to square one](#Back-to-square-one)\n",
    "- [Let's get fancy](#Let's-get-fancy)\n",
    "- [Thinking outside the box](#Thinking-outside-the-box)\n",
    "- [I love when things are transparent, free and clear](#I-love-when-things-are-transparent,-free-and-clear)\n",
    "- [Markers -- All good things come in threes!](#Markers----All-good-things-come-in-threes!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Back to square one"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[⬆](#Sections)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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XyYxSIYTILj3djHKpUAG++sqrAt1RDu98JNxPtrMTwolsNnjxRbOD0Y8/QoECVlfkEhLq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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1057397f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.arange(10)\n",
    "\n",
    "for i in range(1, 4):\n",
    "    plt.plot(x, i * x**2, label='Group %d' % i)\n",
    "\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's get fancy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[⬆](#Sections)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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aa2bvmU39ifUJ8A8gamAU3et1l0D3MNJSF8IKiYnw7LNw4oSZKerhuxMdij/E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\nkVAXQggfIqEuhBA+5P8B6qesjyuIkisAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10580beb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(10)\n",
    "\n",
    "for i in range(1, 4):\n",
    "    plt.plot(x, i * x**2, label='Group %d' % i)\n",
    "\n",
    "plt.legend(loc='best', fancybox=True, shadow=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Thinking outside the box"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[⬆](#Sections)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cXHpWOm8te4u2E9rS9aqurHtinQa6Q86cydvjNNSV8qVvv4UOHWxXy/jxULKk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105a00048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.subplot(111)\n",
    "\n",
    "x = np.arange(10)\n",
    "\n",
    "for i in range(1, 4):\n",
    "    ax.plot(x, i * x**2, label='Group %d' % i)\n",
    "\n",
    "ax.legend(loc='upper center', \n",
    "          bbox_to_anchor=(0.5,  # horizontal\n",
    "                          1.15),# vertical \n",
    "          ncol=3, fancybox=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SJw7R8H3hNP+qOfffdD9r+q2REBWWkBGpEGVJWho8/TSkpMCKFeDpaXVFhXIh\n4wJvLn+TFQdXEN47nNZ+ra0uSZRhMiIVoqw4f97s4KIULFrktCH6+8nfafFVC5LTk9kxcIeEqLCc\nBKkQZcGhQ9CunVlQNG8eVKxodUUFprVm0qZJdJrViVHtRjHn8Tl4V/K2uiwhZGpXCJe3YgU8+6zp\nm/vaa2ZE6mROnT9F//D+nE49zaYBm2TjbeFQZEQqhKvSGj76CPr3h++/h9dfd8oQXX5gOU2nNqVp\n7aasf369hKhwONcdkSqlQoFHgFNa6zvtj1UHvgMaAIeBXlrrJPvnRgEvAFnA61rrFSVTuhDiqpKT\nTYAePw6bN0O9elZXVGBpmWm8vfpt5kfPZ87jc+h4U0erSxIiT/kZkX4DXN54cySwUmt9K7Da/jFK\nqcZAb6Cx/TVfKKVk1CtEafrzT2jVCmrWhLVrnTJE9yXsI2h6EH8l/cXOgTslRIVDu27Iaa0jgTOX\nPdwNmGG/PwPoYb/fHZirtc7QWh8GDgCtiqdUIcR1/fQTtG9v9hD98kunW1SktWba9mm0/6Y9g5oP\nYmGvhdTwqGF1WUJcU2EXG/lqrePs9+OA7Cu66wCbcj3vGFC3kMcQQuRXVhaMGQMzZsDixRAYaHVF\nBZaYmsjLi18mJjGGtf3XysbbwmkUedpVa60Bfa2nFPUYQohrOHPGXB8aGWm6FTlhiK49vJamU5tS\nr2o9ol6MkhAVTqWwI9I4pVRtrfVJpdSNwCn748eB3D266tkfu8KYMWNy7gcHBxMcHFzIUoQow3bt\ngsceg27dYOxYqFDB6ooKJCMrg/fWvse0HdOY3m06Dzd62OqSHEpERAQRERFWlyGuQ5kB5XWepFRD\nYHGuVbtjgdNa60+UUiMBH631SPtiozDMedG6wCrgFn3ZQZRSlz8khCioefPMdaGTJpm2f05mY+xG\nBi4ZSN2qdfmm+zey8XY+KKXQWjvfNUwuLj+Xv8wFOgA1lVKxwL+Bj4H5SqkB2C9/AdBaRyul5gPR\nQCYwWBJTiGKWmQkjRkB4OKxaBXffbXVFBZJ0MYm3V79N+L5wxnceT58mfVBOeH2rENnyNSIt9oPK\niFSIwjl1Cnr3Nqtxw8KgenWrK8o3rTXz98znjeVv0O22bnx0/0dUq1zN6rKcioxIHZO0CBTCWWzZ\nAk88Ydr9/ec/UK6c1RXl26Ezhxj8y2COnTvGD71+oI1fG6tLEqLYSLMEIZxBaCg88ghMnAj//a/T\nhGhGVgYYSwQPAAAdLUlEQVSfrP+Ell+3JLhBMNtf3i4hKlyOjEiFcGRpaTB0qNmAe906uP12qyvK\ntw2xGxi4ZCD1qtZj80ub8a/mb3VJQpQICVIhHNXx42Yqt3Zt0y+3alWrK8qXM6lnGLV6FIv+XMTE\nLhN5svGTsphIuDSZ2hXCEUVGmn65jz4KCxY4RYhqrZm7ey4BXwTgptyIfjWaXgG9JESFy5MRqRCO\nRGv47DNzHnTGDOhy+X4Rjulg4kEG/zKYkyknWdh7IUH1gqwuSYhSI0EqhKO4cAEGDYLff4eNG8Hf\n8c8ppmelM37DeMZvHM+ItiMYFjSMCuWcq7uSEEUlQSqEIzh82LT6a9wYNmwAT0+rK7qu9UfXM2jJ\nIBr4NGDry1tp6NPQ6pKEsIQEqRBWW7nSXBs6cqRZoevg5xQTUxMZuWokP8f8zKQuk+h5R085DyrK\nNFlsJIRVtIZPPoF+/Uzf3GHDHDpEtdaE7Q4j4IsA3Mu5Ez04micaPyEhKso8GZEKYYXkZHj+eYiN\nNR2L6tWzuqJrOpB4gFd+foX48/H81OcnWtVtZXVJQjgMGZEKUdr274egIKhWDdaudegQTc9K54N1\nHxA0LYguN3dh68tbJUQtcv681RWIq5EgFaI0LVoE7dqZadyvv4ZKlayu6Koij0TSdGpTNh7byLaX\nt/FWm7co7yaTWKVtxw54+WWH/n2rzJP/FUKUBpvNNJoPDTVhGuS411kmpibyfyv/j2UHljGpyyQe\nv+NxOQ9ayi5cgPnzYcoUOHnSBGl0NNSpY3VlIi8SpEKUtDNnoG9fc15061bw9bW6ojxprZmzew7/\nWvEvegX0IvrVaKpWdPyOSq5k3z6YOhVmzTK/a737Ljz0kNPsUVBmSZAKUZJ27zbXh3btCv/7H1Rw\nzGYFMadjeOXnV0hMTWTxU4tpWbel1SWVGenpZo/2KVNg714YMAC2bYOGDa2uTOSXBKkQJeW772DI\nEPj0UzMidUBpmWmM/W0sk6Im8U77d3gt8DU5D1pKDh82p8mnT4c77oBXXoEePcDd3erKREHJ/xgh\nituFCzBqFCxebJotNG1qdUV5Wnt4LQOXDOS2mrexfeB26nvXt7okl5eVBUuXmunbjRtNH46ICKfa\nHU/kQYJUiOK0Zg289JI5wbVlC9SoYXVFVzh94TTDVw5n5V8rmfzQZHrc3sPqklzeyZNm5PnVV2ZX\nvEGDzGIiDw+rKxPFQS5/EaI4JCWZAO3fH0JCYM4chwtRrTUzf59JwBcBVK1YlejB0RKiJUhr83tV\nr15m6vbIEfjxR4iKMr04JERdh4xIhSiqn36CV1+Fbt3gjz8ccu/QtYfX8vaat7mYeZGfn/6Z5nWa\nW12Sy0pMNDvgTZ1q1pa98oo5F+rtbXVloqRIkApRWHFx8Prr5or5sDC4916rK7rC9r+38/bqt9l/\nej/vdXyPp5o8RTk3uZaiuGkNmzeblbfh4WaR9vTp0LatQ7dPFsVEpnaFKCitzYV+d90FN91k9g91\nsBDdf3o/vX/oTdewrjx666PsG7KPvnf1lRAtZikp5rznPffAM89AQADExMDs2aaBlYRo2SAjUiEK\n4sgRGDjQjEaXLjU/QR3IsXPH+E/Efwj/M5w3g94ktFsonu6Ov7eps9m920zdzp0LHTqYTXw6dQI3\nGZqUSfLXLkR+2Gzw2WfQvLn5ybl5s0OFaMKFBN5a/hZ3T72bmh412T9kP6Paj5IQLUYXL5qRZtu2\n0KUL1KoFu3aZBUSdO0uIlmUyIhXievbtgxdfNPfXr3eoi/6S05L5dNOnhESF0DugN3+88gc3VrnR\n6rJcSkyMmb6dMQOaNYPhw8050PLy01PYye9QQlxNRgZ8+KE52fXUU7BuncOE6MXMi0zcNJFGkxux\n//R+ol6M4vNHPpcQLSYZGbBwITzwwD8LhjZuhOXLTfchCVGRm/xzECIv27aZpqc33mjuN2hgdUUA\nZNoymfn7TP6z9j/c7Xs3K59dyZ2+d1pdlsvYtctM386ZA/7+pnFCz54OvdudcAASpELklpoKY8bA\nt9/CuHGmR64DLL3UWrNw70L+36//jxs8b2Buz7m08WtjdVku4dgxc/XS7Nlw9qxZfbtyJTRubHVl\nwllIkAqRbe1a052oWTMzNHGA7c601qz6axVvr3mbLFsWnz74KQ/e/KDsD1pE587BggUmPHfsMKPO\nyZOhfXtZNCQKTmmtS/+gSmkrjitEns6dgxEjTJP5zz4zJ8EcQNSxKEatHsXx5OO83/F9nmj8BG5K\nfsoXVkYGLFtmwnPZMrjvPjPh8MgjzjN1q5RCay2/RTkYGZGKsu3nn00Pty5dTHs/Hx+rK2LPqT28\ns+Ydtv29jdEdRtO/aX/Z2qyQtDa9bWfPNk3ib7vNhOeUKVC9utXVCVch/ztF2RQfD8OGwaZN5nzo\nffdZXRGHkw4zOmI0yw4sY0TbEcx7Yh6VyjvJUMnBxMSYBUOzZ0O5cma7sk2bzAIiIYqbBKkoW7SG\nefPgjTfM0GT3bsu34YhLieO/6/5L2B9hDGk5hJjXYqha0fEa3zu6+Hizl/rs2XDokLliad4800ND\nTimLkiRBKsqOY8fMNO6RI+Z8aMuWlpaTdDGJ//32P6Zum0q/u/qx79V91PKsZWlNziY1FRYtMuEZ\nGWnOd44eba7/lGs9RWmRf2rC9dlspjXNu++a3VoWLAB3d8vKuZBxgclRkxm3cRzdbu3GjoE7qO9d\n37J6nE1WlllgPWuW2WmlVSszuRAWBlWqWF2dKIuKFKRKqcPAOSALyNBat1JKVQe+AxoAh4FeWuuk\nItYpROHExJj2funpEBFhtuewSEZWBtN3TOf9de/Tul5r1vVfxx217rCsHmeT3SwhLAxuuMGE54cf\nmp4ZQlipSJe/KKUOAc211om5HhsLJGitxyqlRgDVtNYjL3udXP4iSlZmJkyYAGPHmpHokCFm1YkF\nbNrGvD/m8e9f/41/NX8+vP9DWtRpYUktziavZgnZ25WVRXL5i2Mqjqndy/9SuwEd7PdnABHASIQo\nLb//Di+8YK5v2LLF7BlqAa01P8f8zDtr3qFS+Up89ehX3HeT9auDHd3Zs6bP7axZ5q+yZ09zeW+7\ndtIsQTimoo5I/wLOYqZ2v9Raf62UOqO1rmb/vAISsz/O9ToZkYrid/Ei/Pe/5nzoJ59A//6WLNfU\nWhNxOIJ3f32XMxfP8MF9H9D9tu7Sjega0tNNQ3hnbpZQGmRE6piKOiJtq7X+WylVC1iplNqX+5Na\na62UyjMxx4wZk3M/ODiY4ODgIpYiyrTffjPnQhs3NsMYC06cpWakErY7jJDNIaRnpTOq3SieufMZ\nyrlZM6Xs6DIzza50339vmiXcfrs0S7hcREQEERERVpchrqPYWgQqpUYDKcBLQLDW+qRS6kbgV631\n7Zc9V0akonicPAnvv292V5482cwDlrJj547xxZYvmLZ9Gi3rtmRo4FA6+XeSdn55uHABVqwwq22X\nLDGb6jz2GDz9tDRLyA8ZkTqmQv9PV0p5KKWq2O97Ap2B3cAi4Dn7054DwotapBBXOHUK/vUvMwIt\nX9609yvFENVasyF2A31+6MNdU+4iJT2F9S+s5+enf6bzzZ0lRHNJSDDNo3r0gNq1ze87zZvD9u1m\nh7r/9/8kRIVzK8rUri/wo/28T3lgjtZ6hVJqKzBfKTUA++UvRa5SiGzx8fC//8H06WYYs3s31K1b\naodPy0xj/p75hGwO4UzqGV5r9Rpfdv0S70repVaDMzh0yIw6f/rJ7K7ywAPwxBMQGirTtsL1yO4v\nwjkkJJj9Qb/+Gvr0gVGjoF69Ujv8yZSTTN06lS+3fUmTG5rweqvXebjRw3L+005r2LnThGd4OPz9\nN3TrZkah998PlStbXaFrkKldxySdjYRjO30axo+HL7+EXr3MT2s/v1I7/NYTWwmJCmHx/sX0atyL\nVc+uIuCGMnoR42UyM01bvuzwrFDBnO/84gsICrLssl0hSp0EqXBMiYmmocKUKWZOcPt2szKlFGRk\nZfDjvh+ZFDWJY+eO8WrLV5nYZSLVK8uc5Pnz5jKV8HCzA52/vxl1/vKLOV0tV/iIskiCVDiWM2fg\n00/NsOaxx8xqlIYNS+XQCRcS+Hrb13yx9Qtu8rmJN4PepPvt3cv8XqDx8abHf3i46bIYGGjC84MP\nSnVyQAiHVbZ/QgjHkZQEEyeaFjbdu8PmzaW2lHNX3C5CokJYsHcBPW7vwaI+i2h2Y7NSObajOnjQ\nLBQKDzc9bjt3NqemZ850iL3PhXAoEqTCWmfPwqRJ5pqIrl0hKgpuvrnED5tly2Lx/sVMiprE/tP7\neaXFK/w55E9u8LyhxI/tiLQ2s+fZ5zvj481ioZEjTZch6S4kxNVJkAprnDsHISEmRB9+GDZuhFtu\nKfHDnkk9Q+iOUD7b8hm+nr4MDRxKz8Y9cS9n3bZqVsnIgHXr/rlMpVIlM2X75Zdm+lYWCwmRPxKk\nonQlJ5vR58SJ8OCDprXfrbeW+GH3JewjJCqEuX/M5eFGDzOv5zwC6wWW+HEdTUqK6WUbHm4WCDVq\nZMJz+XLTok8WCwlRcBKkonSkpJjznxMmmKvz160zP7lLkE3bWHZgGZOiJrHz5E4GNh/InsF7qFOl\nToke15HYbOaKodWrYdUqM/Bv3dqE5yeflGovCyFcljRkECXr/Hn4/HNzLWjHjvDvf5vrJEpQcloy\n3+78lsmbJ+Pl7sXQwKH0btKbSuVd/0Sf1vDXXyY0V6+GNWugZk3o1Mk0RrjvPvCWJkxOSxoyOCYZ\nkYqSceGCuYRl3Di4917zE72Ed2M+mHiQyZsnM/P3mdzvfz/Tu02nXf12Lr99WVyc+fZmjzrT001w\nPvKImQAoxQZQQpRJEqSieF24AFOnmn64bdvCypVw550ldrhMWyZrDq1h8ubJbIzdyIBmA9g5aCf1\nveuX2DGtlpJiZsazR51HjkCHDiY833pLznUKUdokSEXxSE01yz3HjjX94ZYtg7vvLpFDZdmyiDwa\nyfw981mwdwH1qtZjYPOBfPfEd3hU8CiRY1opI8NcFZQdnDt2QMuWJji//BJatDAb4AghrCH//UTR\nXLwIX31lVq60bGmWgjZtWuyHybJl8Vvsb8zfM58fon+gTpU69AroxYYXNnBz9ZK/7rQ02WxmV7js\nqdrISLO6tlMnePddaNcOPFzv9wUhnJYsNhKFc/EiTJsGH38M99wDY8aYP4uRTdvYELshJzxv8LyB\nXgG9eLLxkzSq0ahYj2W1w4f/Cc41a6BqVbM4qFMns0arRg2rKxSOQBYbOSYJUlEwaWlmU8kPPzRT\nt2PGmLnFYmLTNjYd28T8PfP5Pvp7alSukROet9W8rdiOY7WEBPj113/CMzn5n+C8//5S688vnIwE\nqWOSqV2RP/v3w+zZ8O230KQJLFgArVoVy1trrYk6HpUTnlUrVqV3QG9WPbuKO2rdUSzHsNqFC2aK\nNjs4Dx6E9u1NaL76qvmWygIhIZyTjEjF1Z06Bd99ZwL06FF46il49lloVvSG7lprtpzYkhOeHhU8\n6NW4F70CernEfp+ZmbB16z8LhLZsMTPf2aPOVq3M/p1CFISMSB2TBKm41IULpvHq7Nmmfd+jj0Lf\nviYBirg0VGvNtr+3MX/PfObvmU/F8hVzwrPJDU2c+nrPEydg0yazujYqyuz+5u//z1TtvfeCl5fV\nVQpnJ0HqmCRIBWRlmRN2s2ebEA0KMuHZvXuRf/prrdlxckdOeJZzK5cTnnf53uWU4XnhggnKqKh/\nwjM11TR6Dww0376WLaFaNasrFa5GgtQxSZCWVVrD77+b8AwLgzp1THj26QO1axfxrTW/x/2eE54a\nnROeTWs3darwtNngzz//GWlu2mQ+btLEBGZ2eN58s5zjFCVPgtQxSZCWNbGxJjhnzTItcvr2hWee\ngTuKtqhHa83uU7tzwjPDlpETnvfceI/ThGdCwqUjzS1bzMgy92izaVPZn1NYQ4LUMUmQlgVJSWaV\n7ezZsGsXPPGEWTTUpg24uRXprfec2mPCM3o+FzIu5IRnizotHD4809LMzii5R5sJCWZaNvdo84ay\nude3cEASpI5JgtRVpafD0qUmPFesMKte+vY1m2hXrFikt94bvzcnPJPTknmy8ZP0CuhFq7qtHDY8\ntYZDhy5dELR7t+kYlD3SDAw0fWqL+LuFECVGgtQxSZC6Eq3NhpOzZ8P8+Wa7smefNSPQIqx8OXX+\nFJFHIll3ZB2rD60m6WJSTngG1gvETTle8iQlmWnZ3NO07u6XjjSbN5eVtMK5SJA6JglSV5DdLGHO\nHJMWzz4LTz8NDRsW+K201hw5e4R1R9YReSSSyKORxJ2Po61fW9rXb0+Hhh1oVbeVQ4VnZqYZXeae\noo2NNddt5h5tynZiwtlJkDomCVJnlbtZwpEjlzZLKMD0qk3b2Bu/l8ijJjTXHVlHRlYG9za4l/b1\n23Nvg3tpckMTyrmVK8EvJp+12sz07J49l97274f69S8dbTZpIg0PhOuRIHVMEqTOpBiaJWTaMtnx\n946c0Fx/dD3elbxzQrN9/fbcUv0WS8912mzmd4PLA3PfPtO8PSDg0tsdd0CVKpaVK0SpkSB1TBKk\nju7yZgmBgSY8e/TI1wm+1IxUNh/fbKZqj0ay6dgmGvg0uCQ461atWwpfyJW0Np0HLw/MvXvBx+fK\nwGzc2OyKIkRZJUHqmCRIHVFSEmzebFbbFrBZwtmLZ9kQuyEnOHec3EGTG5pwb/17ad+gPW392lLD\no3T35NIajh+/NCz/+MMEppdX3oHp41OqJQrhFCRIHZMEqdVyr5TJXl567JhZKXPvvebcZ+PGV315\nXEqcOb95JJJ1R9cRczqGlnVb5gRnUL0gvNxLZ2mq1vD331eOMKOjzRU3ucOySRPzZVWvXiqlCeES\nJEgdkwRpadLahGTu5aU7dpjNJ3O3zgkIyPOcp9aaw0mHc85vRh6N5NT5Uzkrau9tcC/N6zTHvZx7\niX8ZcXFXBuaePabsy0eYAQFQs2aJliREmSBB6pgkSEtSSorpbp67C0BGxqXLS1u2BG/vPF+ee0Vt\ndnCW1ora1FRz/vLoUbPwJ/t26JAZYULegSldgIQoORKkjkmCtLjYbOakX+4p2gMH4K67Lr2YsWHD\nKy5PSc1I5eCZg8ScjiEmMSbnzz9O/VEiK2q1hjNnLg3I3IF59Kg5TevnZy4radDg0lvjxuDrK03a\nhShtEqSOSYK0sOLiLp2i3brVDMdyT9HefbdpkABczLzIX2f+uiIsYxJjiD8fT0OfhjSq0YhG1e23\nGo1oXKsxdarUKXBpWVlmf8zLwzH3fTe3S8Mxd2DWr2/WNEmrPCEciwSpY5IgzY+LF825zNxTtElJ\n/4RmYCC0akV6tap5huWBxAOcTDlJfe/6V4Rlo+qNqO9dv0DTs9nTrnkF5JEjJkRr1Mg7ILPvX2U2\nWQjhwCRIHVOJBKlSqgswESgHTNNaf3LZ5x03SLU2U7K5p2ijo00388BAMlu1IPb2OkT7ZBCTZKZj\nD5w5QMzpGI4nH8evql+eYdnApwHl3a7dNCEz0+Tz6dOQmGgGvXmF5dmzpt3d1UaTfn5F7ksvhHBA\nEqSOqdiDVClVDvgT6AQcB7YAT2mt9+Z6jnVBqjWcO2eSKjuxEhOJWLaM4FOnICoK7eVFSrMAjt1e\nlz/8Pdl0QzrR5w8RczqG2HOx1K1SN8+wbOjTkArlKpCVZcIu++0v/zOvx06fNmuTvL3NaLJ6dVAq\ngubNg68IS19f66ZdIyIiCA4Otubg1+CIdUlN+SM15Z8EqWPKX1+5gmkFHNBaHwZQSs0DugN7r/Wi\nAtPaJM81EkonJmJLiMd2Oh59OhG3xETczp7DVtGdNG8vLlb14ELVyqR4uRN6Mp7IB29kWRtvtnGC\n2l6KW6pn0MCrEbXLN+IB74fp6dEI9ws3cTbRncSDcHozbEmE5ZcF4rlzpmVddiBWr/7P/Ro1zNZd\nQUFXfs7H59KAHDMmgjFjgov121ZUjvoDxhHrkpryR2oSzq4kgrQuEJvr42NA4FWfrbXpIZuYSMap\nk6SeOk5a/N9knDpJVkI8toQESExEnT5DuaSzVDibQqWzKXgkp5JZzo1zXu6c9SjPmcpuJFZWnK6k\nia+cxamKGcRVTuNMlXIk+1bmvIcXKZVrcb7SLVDOmwo2L8rZvCiX5UW5zCrERexk76khZOxqhN9x\nf87EVyQiCTw9rx6I/v7m6pXLP1etGpSzvse7EEKIUlASQZqvOds9NT2ofjGDahcz0QpOV4bEynC6\nUnnOVKxAYoWKnK5QmcTyHiS4eZFUoSpnvOtytnp1zpWvQXL5mmhVHXe8cFdeVKQKFZUXldy8qFzO\ni8rlquBR3hOvCu5UV+CeBe7p4I5ZSFuxovkz+7ayzhgG9O52RSDKDiJCCCGupSTOkQYBY7TWXewf\njwJsuRccKaUcdKWREEI4NjlH6nhKIkjLYxYb3Q+cADZz2WIjIYQQwlUU+9Su1jpTKTUEWI65/GW6\nhKgQQghXZUlDBiGEEMJVlOrViEqpLkqpfUqpGKXUiNI89tUopUKVUnFKqd1W15JNKeWnlPpVKbVH\nKfWHUup1B6ipklIqSim1UykVrZT6yOqasimlyimldiilFltdC4BS6rBSape9ps1W15NNKeWjlPpB\nKbXX/ncYZHE9t9m/R9m3sw7yb32U/f/ebqVUmFLK8vYmSqmh9nr+UEoNtboecalSG5Hmp1GDFZRS\n7YEUYKbW+k4ra8mmlKoN1NZa71RKeQHbgB4O8L3y0FpfsJ8HXw/8S2u93sqa7HW9CTQHqmituzlA\nPYeA5lrrRKtryU0pNQNYq7UOtf8demqtz1pdF4BSyg3zc6GV1jr2es8vwToaAmuAO7TWaUqp74Bf\ntNYzLKypCTAXaAlkAMuAQVrrg1bVJC5VmiPSnEYNWusMILtRg6W01pHAGavryE1rfVJrvdN+PwXT\nzKLg3euLmdb6gv2uO+b8t+VBoZSqBzwMTAMcaTWjI9WCUsobaK+1DgWzlsFRQtSuE3DQyhC1O4cJ\nKw/7LxsemIC30u1AlNb6otY6C1gLPG5xTSKX0gzSvBo11C3F4zsl+2/IzYAoaysxowal1E4gDvhV\nax1tdU3Ap8BwwGZ1IbloYJVSaqtS6iWri7G7CYhXSn2jlNqulPpaKeVhdVG59AHCrC7CPoswHjiK\nueogSWu9ytqq+ANor5Sqbv87ewSoZ3FNIpfSDFJZ1VRA9mndH4Ch9pGppbTWNq11U8x/4nuVUsFW\n1qOU6gqc0lrvwLFGgG211s2Ah4BX7acPrFYeuAf4Qmt9D3AeGGltSYZSyh14FPjeAWq5GRgGNMTM\nAnkppZ6xsiat9T7gE2AFsBTYgWP94ljmlWaQHgf8cn3shxmVijwopSoAC4DZWutwq+vJzT4l+DPQ\nwuJS2gDd7Ock5wL3KaVmWlwTWuu/7X/GAz9iTmtY7RhwTGu9xf7xD5hgdQQPAdvs3y+rtQA2aK1P\na60zgYWYf2eW0lqHaq1baK07AEmY9SbCQZRmkG4FGimlGtp/A+0NLCrF4zsNpZQCpgPRWuuJVtcD\noJSqqZTysd+vDDyA+c3YMlrrt7XWflrrmzBTg2u01v2srEkp5aGUqmK/7wl0BixfEa61PgnEKqVu\ntT/UCdhjYUm5PYX5RcgR7AOClFKV7f8POwGWn8JQSt1g/7M+8BgOMA0u/lESvXbz5KiNGpRSc4EO\nQA2lVCzwb631NxaX1RboC+xSSmWH1Sit9TILa7oRmGFfXekGzNJar7awnrw4wukDX+BH8zOY8sAc\nrfUKa0vK8Rowx/6L7EHgeYvryf5loxPgEOeStda/22c1tmKmT7cDX1lbFQA/KKVqYBZCDdZan7O6\nIPEPacgghBBCFIFF20MLIYQQrkGCVAghhCgCCVIhhBCiCCRIhRBCiCKQIBVCCCGKQIJUCCGEKAIJ\nUiGEEKIIJEiFEEKIIvj/dx8tPSyMKYkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1066024e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.subplot(111)\n",
    "\n",
    "x = np.arange(10)\n",
    "\n",
    "for i in range(1, 4):\n",
    "    ax.plot(x, i * x**2, label='Group %d' % i)\n",
    "\n",
    "ax.legend(loc='upper center', \n",
    "          bbox_to_anchor=(1.15, 1.02),\n",
    "          ncol=1, fancybox=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I love when things are transparent, free and clear"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[⬆](#Sections)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Xsfnlzbzd4m0K+8mbe3fbuhVeftnx1375DQnhDjabWYRr2jQT7M08dxx3QlIC\n/17+b5buX8rYdmN54q4npN/czS5dgrlzYeJEOHHChHp0NFSpcvNjJdSFcLUzZ6B3b9OPvmkTBAff\n/BgLaK2ZtXMW/1r2L7rV7Ub0a9GUKur5M1l9yd69MGkSfPeded1/911o3z5367dJqAvhSjt3mvHn\nHTrA55977HZzMfExvLr4VRKSElj49EIaV21sdUkFxuXLZr+TiRNhzx7o0wc2b4aaNfP2fBLqQrjK\n999D//7w5Zempe6BUtJSGPnHSMZuGMs7rd/h9aavS7+5mxw6ZC6rTJ0Kd90Fr74KXbqAv3/+nld+\ne0I426VLMHQoLFxoJhbVr291RdladWgVfRf15Y7yd7Cl7xaql65udUk+Lz0dliwxXSzr1pk5ZxER\nzl1RWUJdCGdauRJeesl0iG7cCOXKWV3RdeIvxTNo+SCW/7Wc8e3H0+XOLlaX5PNOnDAt8q++Misp\nv/KKuRAaEOD8c8mQRiGcITHRhPnzz8O4cTBrlscFutaaGdtnUHdCXUoVLUV0v2gJdBfS2rzGd+tm\nulcOH4affoING8y8M1cEOkhLXYj8W7AAXnsNOnWCXbs8cu3zVYdWMWzlMJLTklncczENqzS0uiSf\nlZBgVk2eNMlcF3/1VdN3Xrq0e84voS5EXp08CW+8YWaHhIVBmzZWV3SdLX9vYdiKYeyL38f797/P\n0/WeppCfd+xv6k20NptTTZxoRrJ06GC6W1q2dP9yPtL9IkRuaW0GEt99N9xyi1n/3MMCfV/8Prr/\n2J0OYR3oeHtH9vbfS++7e0ugO9mFC6af/N57oVcvqFsXYmJg5kwzcdiKOVvSUhciNw4fhr59TSt9\nyRLzv9mDxJ6L5b2I9wj/M5y3mr3FtE7TKOHv+Wuze5udO033yuzZcN99ZrHNtm3BzwOayR5QghBe\nwGYz28w1bGj+F0dFeVSgn750mrd/fZt7Jt1D+YDy7Ou/j6Gth0qgO1FysmmBt2xpdhmsUAF27DAX\nPx9+2DMCHaSlLsTN7d0L//ynub1mjXMHFefT+ZTzfLn+S8ZtGEf3ut3Z9eouKpesbHVZPiUmxnSx\nTJ8ODRrAoEGmz7ywh6anh7y2COGBUlPh449N5+jTT8Pq1R4T6MlpyYxZP4ba42uzL34fG/65gf89\n9j8JdCdJTYX58+Ghh65c7Fy3Dn791cz69NRAB2mpC5G9zZvNIhyVK5vbNWpYXREAabY0ZmyfwXur\n3uOe4HtS7cmSAAAWpklEQVRY/sxy/hH8D6vL8hk7dpgullmzzL7fr7wCXbt69ArJ15FQFyKrpCQY\nMQK+/RZGjTJrtnjAsrNaa+bvmc///f5/VCxRkdldZ9MipIXVZfmE2FgzInXmTDh71oxiWb7c7AHu\njSTUhciwapWZFdqggWmyecASuVprfvvrN4atHEa6LZ0vH/mSR259RNY3z6dz52DePBPkW7ea1vj4\n8dC6tedc8MwrpbV2/0mV0lacV4hsnTsHgwebBbj++1/TaeoBNsRuYOiKoRw7f4wP7v+AJ+s8iZ/y\n8sSxUGoqLF1qgnzpUnjgAfNG7LHHvKd7RSmF1jrHV3RpqYuCbfFiM4+7XTszxT8oyOqK2H1qN++s\nfIfNf29m+H3Deb7+87Icbh5pbdZamTnTLKB1xx0myCdOhLJlra7ONeQvRRRMcXFmn9D1603/+QMP\nWF0RhxIPMTxiOEv3L2Vwy8HMeXIOxQp7SRPSw8TEmIudM2eaXYOeecb8qmvVsroy15NQFwWL1jBn\nDrz5pmmy7dzpuuXyHHTywkk+XP0hYbvC6N+4PzGvx8g2cnkQF2f2JZk5Ew4eNKNQ58wx88UK0iUI\nCXVRcMTGmq6Ww4dN/3lja7dsS0xO5PM/PmfS5kk8e/ez7H1tLxVKVLC0Jm+TlGT28Z45EyIjTf/4\n8OFmfLknjyV3pQL6bYsCxWYzUwLffdesqjhvXv73DMuHS6mXGL9hPKPWjaLT7Z3Y2ner7DqUC+np\nZqDSd9+ZFRGbNDFvusLCoGRJq6uzXr5CXSl1CDgHpAOpWusmSqmywPdADeAQ0E1rnZjPOoXIm5gY\nM8X/8mWzb1jdupaVkpqeytStU/lg9Qc0r9ac1c+v5q4Kd1lWj7fJmBgUFgYVK5og//hjMz9MXJGv\nIY1KqYNAQ611Qpb7RgKntdYjlVKDgTJa6yHXHCdDGoVrpaXB6NEwcqRpoffvb66YWcCmbczZNYf/\n/P4fapWpxccPfkyjKo0sqcXbZDcxKGOJ24LIXUMarz1BJ+A+++3pQAQwBCHcZft2ePFFM2Zt40az\n5rkFtNYsjlnMOyvfoVjhYnzV8SseuMX6UTae7uxZs+7Kd9+ZX2XXrmb6QKtW3j8xyB3y21L/CziL\n6X6ZrLX+Wil1Rmtdxv51BSRkfJ7lOGmpC+dLToYPPzT95599ZvYLtWDYg9aaiEMRvPv7u5xJPsNH\nD3xE5zs6yyzQHFy+bBbL8uaJQe7gjpZ6S63130qpCsBypdTerF/UWmulVLbpPWLEiMzboaGhhIaG\n5rMUUaD98YfpO69TxzTvLOhoTUpNImxnGOOixnE5/TJDWw2l1z96yW5DN5CWZlYy/uEHMzHozjt9\nf2JQbkVERBAREZGrY5y2TIBSajhwAXgJCNVan1BKVQZ+11rfec1jpaUunOPECfjgA7NTwfjx5r26\nm8Wei2XCxglM2TKFxlUbM6DpANrWaitT+rNx6RIsW2ZGrSxaZBa/fPxx6NmzYEwMyi9HWup5/qtT\nSgUopUrab5cAHgZ2Aj8Dz9kf9hwQntdzCHFDp07Bv/5lWuaFC5sp/m4MdK01a4+upcePPbh74t1c\nuHyBNS+uYXHPxTx868MS6FmcPm0m7XbpApUqmdfehg1hyxazqvH//Z8EujPlp/slGPjJ3k9YGJil\ntV6mlNoEzFVK9cE+pDHfVQqRIS4OPv/cbNXes6eZEVq1qttOn5KWwtzdcxkXNY4zSWd4vcnrTO4w\nmdLFSrutBm9w8KBpjS9YYFZBfOghePJJmDZNulZcTVZpFN7h9GmzvvnXX0OPHjB0KFSr5rbTn7hw\ngkmbJjF582TqVazHG03e4NHaj0p/uZ3WsG2bCfLwcPj7b+jUybTOH3wQihe3ukLfIKs0Cu8XHw9f\nfAGTJ0O3biY5QkLcdvpNxzcxbsM4Fu5bSLc63fjtmd+oW7GADpK+RlqamZqfEeRFipj+8QkToFkz\ny6YFFHgS6sIzJSSYyUMTJ5r37Vu2uG1LudT0VH7a+xNjN4wl9lwsrzV+jTHtxlC2uPQbXLxohh6G\nh5tVi2vVMq3xX34xlzdk1Kb1JNSFZzlzBr780jT3Hn/cXEmrWdMtpz596TRfb/6aCZsmcEvQLbzV\n7C0639m5wK9lHhdn1j8LDzcrLTRtaoL8o4/c+qZJOKhg/7UKz5GYCGPGmKmDnTtDVJTbhkTsOLmD\ncRvGMW/PPLrc2YWfe/xMg8oN3HJuT3XggLnIGR5u1lx5+GFzKWPGDI/YR0TkQEJdWOvsWRg71oxz\n69DBbFNz660uP226LZ2F+xYydsNY9sXv49VGr/Jn/z+pWKKiy8/tibQ2PVwZ/eNxceZC55AhZnan\nzOr0HhLqwhrnzsG4cSbQH30U1q2D225z+WnPJJ1h2tZp/HfjfwkuEcyApgPoWqcr/oWsW4rXKqmp\nsHr1laGHxYqZbpXJk00Xi1zo9E4S6sK9zp83rfIxY+CRR8z0/ttvd/lp957ey7gN45i9azaP1n6U\nOV3n0LRaU5ef19NcuGDWVgkPNxc3a9c2Qf7rr2aavlzo9H4S6sI9Llww/eWjR5uZKKtXmxRxIZu2\nsXT/UsZuGMu2E9vo27Avu/vtpkrJKi49ryex2cwo0BUr4LffzBui5s1NkH/2mVvnbQk3kclHwrUu\nXoT//c+MNb//fvjPf8zYNxc6n3Keb7d9y/io8QT6BzKg6QC61+teIDZx1hr++ssE+IoVsHIllC8P\nbduaSUAPPAClZfKr15LJR8I6ly6ZYYmjRkGbNiZdXLyzwYGEA4yPGs+M7TN4sNaDTO00lVbVW/n8\nkrcnT5ofb0Zr/PJlE+KPPWbeGLlx4q3wABLqwrkuXYJJk8z6LC1bwvLl8I9/uOx0abY0Vh5cyfio\n8aw7uo4+Dfqw7ZVtPr3n54ULpvcqozV++DDcd58J8rfflr7xgk5CXThHUpIZNjFypJkjvnQp3HOP\nS06Vbksn8kgkc3fPZd6eeVQrVY2+Dfvy/ZPfE1AkwCXntFJqqhnpmRHiW7dC48YmxCdPhkaNzEKV\nQoCEusiv5OQrOw01bmyGVNSv7/TTpNvS+ePoH8zdPZcfo3+kSskqdKvbjbUvruXWsq4f1+5ONptZ\nSTijOyUy0oxSadvWbLfaqhUE+N5rl3ASuVAq8iY5GaZMgU8/hXvvhREjzL9OZNM21h5dmxnkFUtU\npFvdbjxV5ylql6vt1HNZ7dChKyG+ciWUKmUubLZta64vlytndYXCEzhyoVRCXeROSopZFPvjj033\nyogR5v2/k9i0jfWx65m7ey4/RP9AueLlMoP8jvJ3OO08Vjt9Gn7//UqQnz9/JcQffNBta5cJLyOj\nX4Tz7NtndgX+9luoVw/mzYMmTZzy1FprNhzbkBnkpYqWonvd7vz2zG/cVeEup5zDapcumW6UjBA/\ncABatzYB/tpr5kcqFzeFM0hLXdzYqVPw/fcmzI8cgaefhmeegQb5X+xKa83G4xszgzygSADd6nSj\nW91uPrFeeVoabNp05eLmxo2mdyqjNd6kiVl/XIjckO4XkXuXLpmFQGbONFP4O3Y0W7w/+GC+h1ho\nrdn892bm7p7L3N1zKVq4aGaQ16tYz6vHkx8/DuvXm1EqGzaYFYNr1brSndKmDQQGWl2l8HYS6sIx\n6emmg3fmTBPozZqZIO/cOd9JpLVm64mtmUFeyK9QZpDfHXy3Vwb5pUsmtDdsuBLkSUlmEaymTc2P\nr3FjKFPG6kqFr5FQFzemNWzfboI8LAyqVDFB3qOH2fI9X0+t2X5ye2aQa3RmkNevVN+rgtxmgz//\nvNICX7/efF6vngnvjCC/9VbpExeuJ6Eurnf0qAnx774zUxN794ZeveCu/F2Q1Fqz89TOzCBPtaVm\nBvm9le/1miA/ffrqFvjGjabFnbUVXr++rC8urCGhLozERDNaZeZMs43Nk0+aC54tWoCfX76eevep\n3SbIo+dyKfVSZpA3qtLI44M8JcWsYJi1FX76tOk6ydoKr1gw980QHkhCvSC7fBmWLDFBvmyZuWLX\nu7fZkKJo0Xw99Z64PZlBfj7lPE/VeYpudbvRpGoTjw1yreHgwasvZu7caWZqZrTAmzY166bk83VO\nCJeRUC9otDYLZs+cCXPnmiVun3nGtMzzcdXu1MVTRB6OZPXh1aw4uILE5MTMIG9arSl+yvNSMDHR\ndJ1k7Urx97+6Bd6woYxIEd5FQr2gyJgYNGuWSa5nnoGePaFmzVw/ldaaw2cPs/rwaiIPRxJ5JJKT\nF0/SMqQlrau35r6a99GkahOPCvK0NNPqztqNcvSoGReetRUuS9AKbyeh7suyTgw6fPjqiUG56AKx\naRt74vYQecQE+OrDq0lNT6VNjTa0rt6aNjXaUK9iPQr5Wb9hpc1mulB27776Y98+qF796lZ4vXoy\nuUf4Hgl1X+OEiUFptjS2/r01M8DXHFlD6WKlMwO8dfXW3Fb2Nkv7xm028zp1bXjv3WsWtqpb9+qP\nu+6CkiUtK1cIt5FQ9wXXTgxq2tQEeZcuDnUIJ6UmEXUsynSnHIlkfex6agTVuCrEq5ayZqNKrc3q\nA9eG9549EBR0fXjXqWNWLxSioJJQ91aJiRAVZUat5HJi0Nnks6w9ujYzxLee2Eq9ivVoU70NrWu0\npmVIS8oFuHcdV63h2LGrg3vXLhPegYHZh3dQkFtLFMIrSKh7g6xX+TKGacTGmqt8bdqYvvIcNmo+\neeGk6Q8/HMnqI6uJiY+hcdXGmSHerFozAv3dM8RDa/j77+tb3tHRZhRl1uCuV898W2XLuqU0IXyC\nhLqn0doEdtZhGlu3msWzs05ZrFs32z5yrTWHEg9l9odHHonk1MVTmSNT2tRoQ8MqDfEv5O/yb+Pk\nyevDe/duU/a1Le+6dc2O9kKI/JFQt9qFC2blp6wzXlJTrx6m0bgxlC6d7eFZR6ZkhLi7RqYkJZn+\n7iNHzEXLjI+DB03LG7IPb5l9KYTrSKi7k81mOomzdqPs3w933331YOmaNa8bcpiUmsSBMweIiY8h\nJiEm899dp3a5ZGSK1nDmzNVhnTW8jxwx3fohIWaoYI0aV3/UqQPBwbKAlRDuJqHuSidPXt2NsmmT\naaZm7Ua55x4zGQhITkvmrzN/XRfcMQkxxF2Mo2ZQTWqXq03tsvaPcrWpU6EOVUpWyXVp6elmfe9r\ngzrrbT+/q4M6a3hXr26ux8p0eSE8i4S6syQnm77vrN0oiYlXArxpU2jShMtlSmUb3PsT9nPiwgmq\nl65+XXDXLlub6qWr56oLJaNrJLuwPnzYBHq5ctmHdcbtG/T4CCE8mGWhrpRqB4wBCgFTtNafXfN1\nzw11rU23SdZulOhos9JT06akNWnE0TurEB2USkyi6TLZf2Y/MfExHDt/jJBSIdkGd42gGhT2y3mC\nUFqaea2Ij4eEBPNmILvgPnvWTHm/USs7JCTfa3YJITyQJaGulCoE/Am0BY4BG4GntdZ7sjzGulDX\nGs6dM6mZkZ4JCUQsXUroqVOwYQM6MJALDeoSe2dVdtUqwfqKl4m+eJCY+BiOnjtK1ZJVsw3umkE1\nKVKoCOnpJngznv7af7O7Lz7eXFctXdq0ssuWBaUiaNgw9LrgDg62rmskIiKC0NBQa06eA0+sS2py\njNTkOEdCPX+bTmavCbBfa33IXsQcoDOwJ6eDck1rk4I5pKVOSMB2Og5bfBw6PgG/hAT8zp7DVtSf\nlNKBJJcK4FKp4lwI9GfaiTgiH6nM0hal2cxxKgUqbiubSo3A2lQqXJuHSj9K14Da+F+6hbMJ/iQc\ngPgo2JgAv14TzufOmWnrGeFctuyV2+XKmeVemzW7/mtBQVeH9YgREYwYEerUH1t+eeofuyfWJTU5\nRmpyLleEelXgaJbPY4GmN3y01mZNk4QEUk+dIOnUMVLi/ib11AnST8dhO30aEhJQ8WcolHiWImcv\nUOzsBQLOJ5FWyI9zgf6cDSjMmeJ+JBRXxBfTxBVP51TRVE4WT+FMyUKcDy7OxYBALhSvwMVit0Gh\n0hSxBVLIFkih9EAKpZXkZMQ29pzqT+qO2oQcq8WZuKJEJEKJEjcO51q1zIjEa79WpgwUsn79KyFE\nAeSKUHeoX2V3+QDKJqdSJjkNrSC+OCQUh/hihTlTtAgJRYoSX6Q4CYUDOO0XSGKRUpwpXZWzZcty\nrnA5zhcuj1Zl8ScQfxVIUUpSVAVSzC+Q4oUCKV6oJAGFSxBYxJ+yCvzTwf8y+GMGpBQtav7N+Fhe\nZQR9une6LpxlpT8hhDdxRZ96M2CE1rqd/fOhgC3rxVKllIdeJRVCCM9mxYXSwpgLpQ8Cx4EorrlQ\nKoQQwjWc3v2itU5TSvUHfsUMaZwqgS6EEO5hyeQjIYQQruHW0c5KqXZKqb1KqRil1GB3nvtGlFLT\nlFInlVI7ra4lg1IqRCn1u1Jqt1Jql1LqDQ+oqZhSaoNSaptSKlop9YnVNWVQShVSSm1VSi20uhYA\npdQhpdQOe01RVteTQSkVpJT6USm1x/47bGZxPXfYf0YZH2c95G99qP3/3k6lVJhSyvKpfEqpAfZ6\ndimlBuT4YK21Wz4wXTH7gZpAEWAbcJe7zp9DXa2BBsBOq2vJUlMloL79diDmGoUn/KwC7P8WBtYD\nrayuyV7PW8As4Gera7HXcxAoa3Ud2dQ1HXgxy++wtNU1ZanND/gbCLG4jprAX0BR++ffA89ZXFM9\nYCdQzJ6jy4Fbb/R4d7bUMyclaa1TgYxJSZbSWkcCZ6yuIyut9Qmt9Tb77QuYiVu5X9nLybTWl+w3\n/TF/XAkWlgOAUqoa8CgwBfCkdSM9qRaUUqWB1lrraWCufWmtz1pcVlZtgQNa66M3faRrnQNSgQD7\noI8AzMx4K90JbNBaJ2ut04FVwBM3erA7Qz27SUnWbI7pRZRSNTHvJDZYWwkopfyUUtuAk8DvWuto\nq2sCvgQGATarC8lCA78ppTYppV6yuhi7W4A4pdQ3SqktSqmvlVIBVheVRQ8gzOoitNYJwBfAEczo\nvUSt9W/WVsUuoLVSqqz9d/YYUO1GD3ZnqMsV2VxSSgUCPwID7C12S2mtbVrr+pg/qDZKqVAr61FK\ndQBOaa234lkt45Za6wZAe+A1pVRrqwvCdLfcC0zQWt8LXASGWFuSoZTyBzoCP3hALbcCAzHdMFWA\nQKVULytr0lrvBT4DlgFLgK3k0IhxZ6gfA0KyfB6Caa2LbCiligDzgJla63Cr68nK/rZ9MdDI4lJa\nAJ2UUgeB2cADSqkZFteE1vpv+79xwE+YrkerxQKxWuuN9s9/xIS8J2gPbLb/vKzWCFirtY7XWqcB\n8zF/Z5bSWk/TWjfSWt8HJGKus2XLnaG+CaitlKppf2XuDvzsxvN7DWW2NpoKRGutx1hdD4BSqrxS\nKsh+uzjwEKbFYBmt9TCtdYjW+hbM2/eVWutnraxJKRWglCppv10CeBhzkctSWusTwFGl1O32u9oC\nuy0sKaunMS/KnmAv0EwpVdz+/7AtYHk3o1Kqov3f6sDj5NBV5Yq1X7KlPXRSklJqNnAfUE4pdRT4\nj9b6G4vLagn0BnYopTKCc6jWeqmFNVUGpiul/DCNge+01issrCc7ntDFFwz8ZN9ysDAwS2u9zNqS\nMr0OzLI3qg4AL1hcT8YLX1vAI649aK2329/tbcJ0cWwBvrK2KgB+VEqVw1zE7ae1PnejB8rkIyGE\n8CGyC6UQQvgQCXUhhPAhEupCCOFDJNSFEMKHSKgLIYQPkVAXQggfIqEuhBA+REJdCCF8yP8DUnW+\nguPZQVUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1055e5860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(10)\n",
    "\n",
    "for i in range(1, 4):\n",
    "    plt.plot(x, i * x**2, label='Group %d' % i)\n",
    "\n",
    "plt.legend(loc='upper right', framealpha=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Markers -- All good things come in threes!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[⬆](#Sections)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Vvffey+OPP570dUQkXHIm5EcNGFVvuq38jvmMHlA3/d/AkwZSNr6Mvt360q1zN0YNGMWS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105d19080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itertools import cycle\n",
    "x = np.arange(10)\n",
    "\n",
    "colors = ['blue', 'red', 'green']\n",
    "color_gen = cycle(colors)\n",
    "\n",
    "for i in range(1, 4):\n",
    "    plt.scatter(x, i * x**2, label='Group %d' % i, color=next(color_gen))\n",
    "\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5RkSCEG+OU7+hHkiGfoj1eFBNXkQkaKrJi4hIikJeRCRgCnkRkYAp5EVEAqaQFxEJmEJe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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105dc8710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itertools import cycle\n",
    "x = np.arange(10)\n",
    "\n",
    "colors = ['blue', 'red', 'green']\n",
    "color_gen = cycle(colors)\n",
    "\n",
    "for i in range(1, 4):\n",
    "    plt.scatter(x, i * x**2, label='Group %d' % i, color=next(color_gen))\n",
    "\n",
    "plt.legend(loc='upper left', scatterpoints=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Cjz9CQIDVERXKtsRG1Mwo0nQaVVQV+j/en7kX5pbrXYlcodizX7TWWil1tb4U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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105872d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itertools import cycle\n",
    "x = np.arange(10)\n",
    "\n",
    "colors = ['blue', 'red', 'green']\n",
    "color_gen = cycle(colors)\n",
    "\n",
    "for i in range(1, 4):\n",
    "    plt.plot(x, i * x**2, label='Group %d' % i, marker='o')\n",
    "\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DzFBKPYN9SiOA1nqrUmoGsBXIBAZL9hbCxTIzYdgwmD0bfvkFbr7Z6oiK5FTa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\nAlsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1055a4f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itertools import cycle\n",
    "x = np.arange(10)\n",
    "\n",
    "colors = ['blue', 'red', 'green']\n",
    "color_gen = cycle(colors)\n",
    "\n",
    "for i in range(1, 4):\n",
    "    plt.plot(x, i * x**2, label='Group %d' % i, marker='o')\n",
    "\n",
    "plt.legend(loc='upper left', numpoints=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
